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Data Governance Challenges
and Best Practices

Learn how to start and continue a successful enterprise data governance program

Understanding data governance challenges

Establishing an enterprise data governance program makes it easier for employees to align and work together toward business goals by giving them the trusted data they need to understand what's happening across the organization. To do that, you need to establish a single set of policies and processes for collecting, storing, and using data. You need to scale that framework and its core processes to reach everyone. And you need to collaborate with everyone, across multiple disciplines, to be active participants in the data governance process.

 

See the benefits of adding intelligence to data governance

Developing and launching a data governance program to support and accelerate data-driven digital transformation is no small feat. If you don't know where to start, it may seem easier not to begin. But failing to govern your data well can lead to dire consequences like regulatory penalties, brand damage, and loss of market share. Getting it right, on the other hand, helps you speed time-to-market, better understand your customers, ensure accurate analytics, and make better decisions in less time.

Ensuring the success of your data governance program requires you to help employees understand how engaging with governance will benefit them, both individually and across every discipline and business function. It also demands that you retool your business environment to give everyone—not just data stewards—access to knowledge about data as well as the data itself.

 

Developing a data governance strategy

Four core processes support every data governance program:

  1. Discovering data

  2. Documenting its definitions, policies, standards, processes, and ownership, and analyzing dependent processes

  3. Applying governance policies, business rules, and stewardship

  4. Measuring and monitoring the results, ideally in real time

In addition, every individual project needs stakeholders in these key roles to ensure that everyone affected by the project is appropriately engaged:

  • Driver: Someone who is dedicated full-time to pushing the project forward

  • Approver: Someone who is accountable for key decisions and provides all necessary resources

  • Contributors: Business and IT subject matter experts who provide necessary context, including business leaders, process owners, and stewards who run the upstream and downstream processes impacted by your initiative, as well as IT architects, analysts, and systems experts

  • Informed: Anyone affected by the project, including both data consumers who will benefit from improved data quality and reliability and people elsewhere in the organization who do not directly benefit but whose behaviors and processes will have to change

IT and business leaders must work hand in hand to make sure each understands the other's goals. A collaborative approach is key to communicating the value and impact of data governance across your entire business. Aligning the wants and needs of different departments is also easier if you establish a steering committee to make strategic decisions about the direction of your enterprise data governance program.

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Executing on data governance

Data governance is not a standalone initiative. Finding and measuring data, and then creating a glossary and dictionary to standardize semantics, is only the beginning. Consider it the equivalent of setting up a new department, one that touches every other department in some way. If you want your data governance practice to thrive, you'll need to establish priorities and determine where to direct its efforts on an ongoing basis. As with any other project you hope to scale to the entire enterprise, it makes sense to start governing data with a project that's small enough to be achievable, but still capable of delivering results. Set achievable goals, define them clearly to keep the project on track, and use both quantitative and qualitative metrics to measure success. Ideally, your project should have demonstrable value, ready-made sponsors, and the potential to scale or expand by creating additional opportunities to extend data governance.

 

Data Governance Examples and Use Cases

After allowing its different divisions to manage their own data for decades, textbook publisher McGraw-Hill Education faced a data quality nightmare when it wanted to combine all its data for an enterprise-wide business intelligence initiative. It created a data governance team and deployed Informatica data governance solutions to help all the stakeholders work together more easily. That led to critical improvements such as standardizing data quality rules and metrics across the company; centralizing a single source of truth and fixing data issues before they propagated downstream; giving stakeholders the autonomy to make changes within their functions; and documenting processes to find inefficiencies, clarify goals, and assign efficiencies. Using the right technology to operationalize data governance has ensured a single reliable source of product data, and customer data is next.

AIA Singapore, an insurance and financial services provider since 1931, wanted to better understand its business and customer data to achieve deeper market insights and connect with customers in more personal ways. It began by setting up a Data Governance Council to develop a framework, policies, processes, and standards for its data governance practice. It then used Informatica solutions to develop a collaborative business glossary, scan and index metadata from core systems to understand how data is used, and monitor data quality in real time. AIA Singapore can now follow data end-to-end through the organization, tracking how it is transformed and providing insurance agents and employees with the reliable data they need to optimize sales, decision-making, and costs.

 

Why Informatica?

The biggest data governance challenge is adapting to changing needs and requirements. Today you may be improving data quality in a single business unit. Tomorrow you may need to bring your entire organization into compliance with new privacy regulations. You need a technology platform that can scale and evolve to incorporate new data and users without compromising speed or effectiveness.

Informatica's platform is modular, interoperable, and scalable, with elastic compute power and storage to govern data at any volume, wherever it is. Informatica also includes artificial intelligence for automated, accelerated data discovery, cataloging, and reporting as well as metadata management so your team can focus on connecting new systems and extracting more value from your data. Our centralized data governance console is designed to connect data lineage to business processes, allowing you to document processes and align workflows across your business. Everyone in business and IT can easily understand their roles in your data governance strategy and see how their use of data aligns with your company's data governance standards.

Learn more about the critical importance of consistently and collaboratively improving the trustworthiness and quality of data across your organization.

 

Data governance resources